def expand_by_selfing(self, pop, recombination_rates):
"""
Specific for plant populations capable of selfing.
Creates an F2 subpopulations generation by selfing the individuals of
'pop'. Works on a population with one or more subpopulations.
:param pop:
"""
# self.odd_to_even(pop)
num_sub_pops = pop.numSubPop()
progeny_per_individual = int(self.operating_population_size / 2)
return pop.evolve(
preOps=[
sim.MergeSubPops(),
sim.PyEval(r'"Generation: %d\n" % gen'),
sim.SplitSubPops(sizes=[1] * num_sub_pops, randomize=False),
],
matingScheme=sim.SelfMating(
subPopSize=[progeny_per_individual] * num_sub_pops,
numOffspring=progeny_per_individual,
ops=[
sim.Recombinator(rates=recombination_rates),
sim.IdTagger(),
sim.PedigreeTagger()
],
),
gen=1,
)
def generate_f_one(self, pop: sim.Population):
"""
A very basic implementation of the F_1 cross between pairs of individuals. Relies on
pre-formatting of desired mating pairs into an ordered list.
[1, 3, 5, 11, 12, 9, 22, 2]
The mating pattern would be:
1x3, 5x11, 12x9, 22x2. Rearranging the order of the indices would change the mating
pairs.
:param pop:
:type pop:
:return:
:rtype:
"""
pop.dvars().generations[1] = 'F_1'
pop.dvars().gen = 1
pairs_of_founders = int(pop.popSize() / 2)
self.odd_to_even(pop)
print("Creating the F_one population from selected founders.")
return pop.evolve(
preOps=[
sim.PyEval(r'"Generation: %d\n" % gen'),
operators.CalcTripletFrequencies(),
sim.PyExec('triplet_freq[gen]=tripletFreq'),
sim.SplitSubPops(sizes=[2] * pairs_of_founders, randomize=False),
],
matingScheme=sim.RandomMating(subPopSize=[1] * pairs_of_founders,
ops=[sim.Recombinator(rates=0.01), sim.IdTagger(), sim.PedigreeTagger()]),
gen=1,
)
def _mate_and_merge(self, pop: sim.Population):
starting_gen = pop.vars()['gen']
print("Initiating recombinatorial convergence at generation: %d" % pop.dvars().gen)
while pop.numSubPop() > 1:
pop.vars()['generations'][pop.vars()['gen']] = 'IG'+str(pop.vars()['gen'] - starting_gen)
self.pop_halver(pop)
self.odd_to_even(pop)
self.pairwise_merge_protocol(pop)
sub_pop_sizes = list(pop.subPopSizes())
pop.evolve(
preOps=[
sim.MergeSubPops(),
sim.PyEval(r'"Generation: %d\n" % gen'),
operators.CalcTripletFreq(),
sim.PyExec('triplet_freq[gen]=tripletFreq'),
sim.SplitSubPops(sizes=sub_pop_sizes, randomize=False),
],
matingScheme=sim.RandomMating(ops=[sim.Recombinator(rates=0.01),
sim.IdTagger(), sim.PedigreeTagger()]),
gen=1,
)
def _generate_f_two(self, pop: sim.Population) -> sim.Population:
"""
Creates an F2 subpopulations generation by selfing the individuals of 'pop'. Works on a population with one
or more subpopulations.
"""
pop.vars()['generations'][2] = 'F_2'
self.odd_to_even(pop)
num_sub_pops = pop.numSubPop()
progeny_per_individual = int(self.selected_population_size/2)
print("Creating the F_two population.")
return pop.evolve(
preOps=[
sim.MergeSubPops(),
sim.PyEval(r'"Generation: %d\n" % gen'),
operators.CalcTripletFreq(),
sim.PyExec('triplet_freq[gen]=tripletFreq'),
sim.SplitSubPops(sizes=[1]*num_sub_pops, randomize=False),
],
matingScheme=sim.SelfMating(subPopSize=[progeny_per_individual] * num_sub_pops,
numOffspring=progeny_per_individual,
ops=[sim.Recombinator(rates=0.01), sim.IdTagger(), sim.PedigreeTagger()],
),
gen=1,
)
vec_env = [uniform(-1, 1), uniform(-1, 1)]
#Creating initial population of size popsize and "caryotype" vecloc
pop = sim.Population(size=[popsize],
loci=vecloc,
infoFields=['migrate_to', 'fitness', 'env'],
lociNames=allele_naming(numchrom, numloc))
#--------------------------Main evolving process------------------------
pop.evolve(
#Initializing sex and genotype
initOps=[
sim.InitSex(),
sim.InitGenotype(freq=[0.5, 0.5]),
],
preOps=[
#Splitting each population into two at 'atgen' generations
sim.SplitSubPops(proportions=[0.5, 0.5], at=atgen),
#Calling function 'migration' for individuals migration
#(Operator Migrator does not allow for varying number of subpopulations)
sim.PyOperator(migration, begin=atgen[0]),
#Selection process
#Create new environmental values for new daughter populations
#Only takes place when fission takes place (atgen)
sim.PyOperator(env_update, at=atgen),
#Set environmental value (env infoField) for each individual in the population
#Takes place at each generation after the first fission
sim.PyOperator(env_set, begin=atgen[1]),
#Selection occures at selected loci according to env information field
sim.PySelector(fit_env, loci=locisel, begin=atgen[1]),
],
#Mating at random (pangamy)
matingScheme=sim.RandomMating(
print('Simulation number ' + str(k))
#Creating initial population of size popsize and "caryotype" vecloc
pop = sim.Population(size=[popsize],
loci=vecloc,
infoFields=['migrate_to', 'fitness', 'env'],
lociNames=allele_naming(numchrom, numloc))
#--------------------------Main evolving process------------------------
pop.evolve(
#Initializing sex and genotype
initOps=[
sim.InitSex(),
sim.InitGenotype(freq=[0.5, 0.5]),
],
preOps=[
#Fisson of population into 16 at 'atgen' generation
sim.SplitSubPops(proportions=[0.0625] * 16, at=atgen),
#Migration using a simple stepping stone model
sim.Migrator(rate=migrSteppingStoneRates(m, 16, circular=False),
begin=atgen),
#Selection process
#Set environmental value (env infoField) for each individual in the population
#Takes place at each generation after the first fission
sim.PyOperator(env_set, begin=atgen),
#Selection occures at selected loci according to env information field
sim.PySelector(fit_env, loci=locisel, begin=atgen),
],
#Mating at random (pangamy)
matingScheme=sim.RandomMating(
#Fixed population size (fixed at 'popsize')
subPopSize=demo,
#Recombination
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# This script is an example in the simuPOP user's guide. Please refer to
# the user's guide (http://simupop.sourceforge.net/manual) for a detailed
# description of this example.
#
import simuPOP as sim
def demo(gen, pop):
if gen < 2:
return 1000 + 100 * gen
else:
return [x + 50 * gen for x in pop.subPopSizes()]
pop = sim.Population(1000)
pop.evolve(preOps=[
sim.SplitSubPops(subPops=0, proportions=[.5] * 2, at=2),
sim.Stat(popSize=True),
sim.PyEval(r'"Gen %d:\t%s\n" % (gen, subPopSize)')
],
matingScheme=sim.RandomSelection(subPopSize=demo),
gen=4)
def simulate(NA, N1, N2, Tbeforesplit, Taftersplit, r2loci, numLoci, K_sel,
Mu_sel, S_sel, L_neut, Mu_neut, Nsample1, Nsample2):
pop = Population(size=NA,
ploidy=2,
loci=[L_neut + 1],
infoFields='fitness')
def getfitness(geno):
# returns fitness of genotype geno at the overdominant locus with selection coeff S_sel
# geno is (A1 A2)
if geno[0] == geno[1]:
return 1 - S_sel # homozygote
else:
return 1 # heterozygote
if useRPy:
plotter = VarPlotter(
'alleleFreq[0][0],alleleFreq[0][1],alleleFreq[0][2],alleleFreq[0][3],alleleFreq[0][4]',
ylim=[0, 1],
ylab='allele frequency',
update=Tbeforesplit + Taftersplit - 1,
saveAs='slocus.png')
else:
plotter = NoneOp()
g = pop.evolve(
initOps=[
InitSex(),
#initially put 5 alleles at the selected locus with equal frequencies
InitGenotype(loci=0, freq=[.1] * 10)
# InitGenotype(loci=0, freq=[0.01, 0.1, 0.4, 0.2, 0.29])
],
preOps=[
#Resize the ancestral population at the time immediatly before the split
sim.ResizeSubPops([0], sizes=[N1 + N2], at=Tbeforesplit - 1),
# split ancestral population in 3 subpopulations only works if NA>N1+N2
sim.SplitSubPops(subPops=0, sizes=[N1, N2], at=Tbeforesplit),
# apply overdominant selection by invoking function getfitness
PySelector(loci=0, func=getfitness),
],
matingScheme=RandomMating(ops=[
#apply recombination between the selected locus and the neutral locus at rate r2loci
Recombinator(rates=r2loci, loci=0),
]),
postOps=[
# apply mutation to the selected locus according to K allele model
KAlleleMutator(k=K_sel, rates=[Mu_sel], loci=[0]),
# apply mutation to the neutral sequence
SNPMutator(u=Mu_neut, v=0, loci=range(1, L_neut)),
#Computes the frequency of each allele at selected locus at the last generation
Stat(alleleFreq=0, step=1),
#output to the screen the frequency of the 4 first alleles at the selected locus
PyEval(
r'"%.0f\t %.3f\t %.3f\t %.3f\t %.3f\t %.3f\n" % (gen, alleleFreq[0][0], alleleFreq[0][1], alleleFreq[0][2], alleleFreq[0][3], alleleFreq[0][4])',
step=100),
plotter,
],
# sets the last generation = Tbeforesplit+Taftersplit
gen=Tbeforesplit + Taftersplit)
#draw two random samples from species 1 and 2 with size Nsample1 and Nsample2
sample = drawRandomSample(pop, sizes=[Nsample1, Nsample2])
#write to file the content of the two random samples
sim.utils.saveCSV(sample, filename='output.txt')
return g
def replicate_recurrent_drift(self, multi_pop, meta_sample_library, qtl,
allele_effects, recombination_rates):
"""
:param multi_pop:
:param meta_pop_sample_library:
:param qtl:
:param allele_effects:
:param recombination_rates:
:return:
"""
for pop in multi_pop.populations():
pop.dvars().gen = 0
sizes = [self.individuals_per_breeding_subpop] \
* self.number_of_breeding_subpops + \
[self.number_of_nonbreeding_individuals]
offspring_pops = [self.offspring_per_breeding_subpop] \
* self.number_of_breeding_subpops + [0]
assert len(sizes) == len(offspring_pops), "Number of parental " \
"subpopulations must equal " \
"the number of offspring " \
"subpopulations"
sampling_generations = [
i for i in range(2, self.generations_of_drift, 2)
]
pc = breed.HalfSibBulkBalanceChooser(
self.individuals_per_breeding_subpop, self.offspring_per_female)
multi_pop.evolve(
initOps=[
sim.InitInfo(0, infoFields=['generation']),
sim.InfoExec('replicate=rep'),
operators.GenoAdditiveArray(qtl, allele_effects),
operators.CalculateErrorVariance(self.heritability),
operators.PhenotypeCalculator(
self.proportion_of_individuals_saved),
operators.ReplicateMetaPopulation(meta_sample_library,
self.meta_pop_sample_sizes),
sim.PyEval(r'"Initial: Sampled %d individuals from generation '
r'%d Replicate: %d.\n" % (ss, gen_sampled_from, '
r'rep)'),
],
preOps=[
sim.PyEval(r'"Generation: %d\n" % gen'),
operators.GenoAdditiveArray(qtl, allele_effects, begin=1),
sim.InfoExec('generation=gen'),
sim.InfoExec('replicate=rep'),
operators.PhenotypeCalculator(
self.proportion_of_individuals_saved, begin=1),
operators.ReplicateMetaPopulation(meta_sample_library,
self.meta_pop_sample_sizes,
at=sampling_generations),
sim.SplitSubPops(sizes=sizes, randomize=False),
],
matingScheme=sim.HomoMating(
sim.PyParentsChooser(pc.recursive_pairwise_parent_chooser),
sim.OffspringGenerator(ops=[
sim.IdTagger(),
sim.PedigreeTagger(),
sim.Recombinator(rates=recombination_rates)
],
numOffspring=1),
subPopSize=offspring_pops,
subPops=list(range(1, self.number_of_breeding_subpops, 1))),
postOps=[
sim.MergeSubPops(),
operators.DiscardRandomOffspring(
self.number_of_offspring_discarded),
],
finalOps=[
sim.InfoExec('generation=gen'),
sim.InfoExec('replicate=rep'),
operators.GenoAdditiveArray(qtl, allele_effects),
operators.PhenotypeCalculator(
self.proportion_of_individuals_saved),
operators.ReplicateMetaPopulation(meta_sample_library,
self.meta_pop_sample_sizes),
sim.PyEval(
r'"Final: Sampled %d individuals from generation %d\n" '
r'% (ss, gen_sampled_from)'),
],
gen=self.generations_of_drift)
def recurrent_drift(self, pop, meta_pop, qtl, aes, recombination_rates):
"""
Sets up and runs recurrent selection for a number of generations for a
single replicate population. Samples individuals at specified
intervals to make a ``meta_pop``.
:param pop: Population which undergoes selection.
:param meta_pop: Population into which sampled individuals are
deposited
:param qtl: List of loci to which allele effects have been assigned
:param aes: Dictionary of allele effects
"""
pop.dvars().gen = 0
meta_pop.dvars().gen = 0
sizes = [self.individuals_per_breeding_subpop] \
* self.number_of_breeding_subpops + \
[self.number_of_nonbreeding_individuals]
offspring_pops = [self.offspring_per_breeding_subpop] \
* self.number_of_breeding_subpops + [0]
assert len(sizes) == len(offspring_pops), "Number of parental " \
"subpopulations must equal " \
"the number of offspring " \
"subpopulations"
sampling_generations = [
i for i in range(2, self.generations_of_drift, 2)
]
pc = breed.HalfSibBulkBalanceChooser(
self.individuals_per_breeding_subpop, self.offspring_per_female)
pop.evolve(
initOps=[
sim.InitInfo(0, infoFields=['generation']),
operators.GenoAdditiveArray(qtl, aes),
operators.CalculateErrorVariance(self.heritability),
operators.PhenotypeCalculator(
self.proportion_of_individuals_saved),
operators.MetaPopulation(meta_pop, self.meta_pop_sample_sizes),
sim.PyEval(r'"Initial: Sampled %d individuals from generation '
r'%d Replicate: %d.\n" % (ss, gen_sampled_from, '
r'rep)'),
],
preOps=[
sim.PyEval(r'"Generation: %d\n" % gen'),
operators.GenoAdditiveArray(qtl, aes, begin=1),
sim.InfoExec('generation=gen'),
operators.PhenotypeCalculator(
self.proportion_of_individuals_saved, begin=1),
operators.MetaPopulation(meta_pop,
self.meta_pop_sample_sizes,
at=sampling_generations),
sim.SplitSubPops(sizes=sizes, randomize=True),
],
matingScheme=sim.HomoMating(
sim.PyParentsChooser(pc.recursive_pairwise_parent_chooser),
sim.OffspringGenerator(ops=[
sim.IdTagger(),
sim.PedigreeTagger(),
sim.Recombinator(rates=recombination_rates)
],
numOffspring=1),
subPopSize=offspring_pops,
subPops=list(range(1, self.number_of_breeding_subpops, 1))),
postOps=[
sim.MergeSubPops(),
operators.DiscardRandomOffspring(
self.number_of_offspring_discarded),
],
finalOps=[
sim.InfoExec('generation=gen'),
operators.GenoAdditive(qtl, aes),
operators.PhenotypeCalculator(
self.proportion_of_individuals_saved),
operators.MetaPopulation(meta_pop, self.meta_pop_sample_sizes),
sim.PyEval(
r'"Final: Sampled %d individuals from generation %d\n" '
r'% (ss, gen_sampled_from)'),
],
gen=self.generations_of_drift)
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# This script is an example in the simuPOP user's guide. Please refer to
# the user's guide (http://simupop.sourceforge.net/manual) for a detailed
# description of this example.
#
import simuPOP as sim
pop = sim.Population(1000)
pop.evolve(preOps=[
sim.SplitSubPops(subPops=0, sizes=[300, 300, 400], at=2),
sim.Stat(popSize=True),
sim.PyEval(r'"Gen %d:\t%s\n" % (gen, subPopSize)')
],
matingScheme=sim.RandomSelection(),
gen=4)
